Short-Time Traffic Flow Prediction on Narrow Roads Based on Improved Whale-Optimized GRU

doi:10.19678/j.issn.1000-3428.0068510

Abstract

Abstract:

To address the unavoidable bottleneck in traffic scenes, the short-time traffic flow prediction of narrow roads is very important for optimizing path planning and improving traffic conditions. For traffic on narrow roads, an Improved Whale Optimization Algorithm (IWOA)-Gated Recurrent Unit (GRU) short-time narrow road traffic prediction model is proposed based on a good node-set initialization population, nonlinear parameter control, and Cauchy variation perturbation. An empirical study is conducted using the Simulation of Urban Mobility (SUMO) dataset. The experimental results show that IWOA has better global performance, convergence speed, and stability compared with WOA-GRU, Particle Swarm Optimization (PSO)-GRU, and Long Short-Term Memory (LSTM). The Root Mean Square Error (RMSE) of the proposed method decreased by 10.96%, 28.71%, and 42.23%, respectively, and Mean Absolute Percentage Error (MAPE) decreased by 13.92%, 46.18%, and 52.83%, respectively, indicating significant accuracy and stability.

Key words: short-time traffic flow prediction, narrow roads, Whale Optimization Algorithm (WOA), Gated Recurrent Unit (GRU), Simulation of Urban Mobility (SUMO) software

摘要：

窄路段作为交通场景中不可避免的瓶颈路段, 其短时车流量预测对优化路径规划、改善交通状况具有重要意义。针对窄路段的时效性, 同时考虑适用模型的准确度, 提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化算法(IWOA)-门控循环单元(GRU)的窄路短时车流量预测模型, 以SUMO(Simulation of Urban Mobility)仿真数据进行了实证研究。对比实验结果显示, IWOA具有较好的全局性、收敛速度且更加稳定。基于IWOA-GRU的窄路短时车流量预测模型, 均方根误差(RMSE)指标相较于WOA-GRU、PSO-GRU、长短期记忆神经(LSTM)网络分别降低10.96%、28.71%、42.23%, 平均绝对百分比误差(MAPE)指标分别降低13.92%、46.18%、52.83%, 有较为显著的准确性和稳定性。

关键词: 短时车流量预测, 窄路段, 鲸鱼优化算法, 门控循环单元, SUMO软件

JIA Shuo, LIN Shiyang, YANG Miaohui, SUN Teng. Short-Time Traffic Flow Prediction on Narrow Roads Based on Improved Whale-Optimized GRU[J]. Computer Engineering, 2025, 51(2): 111-125.

贾硕, 林士飏, 杨苗会, 孙滕. 改进鲸鱼优化GRU的窄路短时车流量预测[J]. 计算机工程, 2025, 51(2): 111-125.

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Figures/Tables 23

Fig.1 Narrow roads traffic meeting scene

Fig.2 Flow chart of IWOA-GRU prediction model

Fig.3 Convergence curve of partial unimodal reference function

Fig.4 Convergence curve of partial multimodal reference function

Fig.5 Convergence curve of partial fixed dimensional multimodal reference function

Fig.6 Box diagram of partial unimodal reference function

Fig.7 Box diagram of partial multimodal reference function

Fig.8 Box diagram of partial fixed dimensional multimodal reference function

Fig.9 Running time of four algorithms

Fig.10 Road network diagram

Fig.11 Narrow road traffic flow time series

Fig.12 Comparison of traffic flow prediction results

Fig.13 Comparison of prediction relative error

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